{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Import tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import graphviz\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from scipy import interpolate as interp\n",
    "from pandas import set_option\n",
    "# set_option(\"display.max_rows\", 10)\n",
    "pd.options.mode.chained_assignment = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "set_option(\"display.max_rows\", 15)\n",
    "set_option('display.width', 200)\n",
    "np.set_printoptions(suppress=True, threshold=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Exploring the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "TrainDataPath = '../data/lithofacies_data/train/'\n",
    "filename_AB = 'GY-1_lithofacies_vectors_0.1.csv'   # lithofacies_vectors.csv  GY-1_lithofacies_vectors_0.1.csv  training_data.csv\n",
    "TrainDataPath = os.path.join(TrainDataPath,filename_AB)\n",
    "print(TrainDataPath)\n",
    "\n",
    "\n",
    "TestDataPath = '../data/lithofacies_data/test/'\n",
    "filename_A = 'YX-58_lithofacies_vectors_0.1.csv'   # lithofacies_vectors.csv  GY-1_lithofacies_vectors_0.1.csv YX-58_lithofacies_vectors_0.02.csv  ALEXANDER D_lithofacies_vectors.csv\n",
    "TestDataPath = os.path.join(TestDataPath,filename_A)\n",
    "print(TestDataPath)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 1=sandstone  2=c_siltstone   3=f_siltstone \n",
    "# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n",
    "# 8=packstone 9=bafflestone\n",
    "\n",
    "facies_colors = None\n",
    "facies_labels = None\n",
    "input_vectors = None\n",
    "DEPTH_col_name = None\n",
    "data_model = 2\n",
    "\n",
    "if data_model == 1:\n",
    "    facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72',\n",
    "        '#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
    "    facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
    "                    'WS', 'D','PS', 'BS']\n",
    "    input_vectors = [\"GR\",\"ILD_log10\",\"DeltaPHI\",\"PHIND\",\"NM_M\",\"RELPOS\"]\n",
    "    DEPTH_col_name = \"Depth\"\n",
    "    \n",
    "if data_model == 2:\n",
    "    facies_colors = ['#632423', '#0070C0','#00B0F0','#75DAFF','#00B050','#FFC000', '#FFFF00']\n",
    "    facies_labels = ['高有机质层状页岩相', '高有机质纹层状页岩相','中有机质纹层状页岩相','低有机质纹层状页岩相',\n",
    "                    '中低有机质块状白云岩相', '低有机质块状介壳灰岩相', '低有机质块状粉砂岩相']\n",
    "    input_vectors = [\"GR\",\"CNL\",\"DT\",\"DEN\",\"MSFL\",\"RS\",\"RD\"]\n",
    "    # 深度列名称\n",
    "    DEPTH_col_name = \"DEPTH\"\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Read Data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "training_data = pd.read_csv(TrainDataPath,engine='python',encoding='GBK')\n",
    "\n",
    "blind =  pd.read_csv(TestDataPath,engine='python',encoding='GBK')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# blind = training_data[training_data['Well Name'] == '34']\n",
    "# training_data = training_data[training_data['Well Name'] != '58']\n",
    "# training_data = training_data[training_data['Well Name'] != '34']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
    "# training_data['Formation'] = training_data['Formation'].astype('category')\n",
    "# training_data['Well Name'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# 标签所在列\n",
    "facies_labels_col = \"Facies\"\n",
    "\n",
    "# adjacent_facies = np.array([[0],[1], [0,1], [1,2,3], [4], [5], [5,6]])\n",
    "adjacent_facies = [[0],[1], [0,1], [1,2,3], [4], [5], [5,6]]\n",
    "#facies_color_map is a dictionary that maps facies labels\n",
    "#to their respective colors\n",
    "facies_color_map = {}\n",
    "for ind, label in enumerate(facies_labels):\n",
    "    facies_color_map[label] = facies_colors[ind]\n",
    "\n",
    "def label_facies(row, labels):\n",
    "    return labels[ row[facies_labels_col] -1]\n",
    "\n",
    "# 如果标签是中文，执行下面一段    \n",
    "# training_data.loc[:,facies_labels_col] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)\n",
    "# training_data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# PE_mask = training_data['PE'].notnull().values\n",
    "# training_data = training_data[PE_mask]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def make_facies_log_plot(logs, facies_colors):\n",
    "    #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by = DEPTH_col_name)\n",
    "    cmap_facies = colors.ListedColormap(\n",
    "            facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    \n",
    "    ztop=logs[DEPTH_col_name].min(); zbot=logs[DEPTH_col_name].max()\n",
    "    \n",
    "    cluster = np.repeat(np.expand_dims(logs[facies_labels_col].values,1), 100, 1)\n",
    "    total_fig_cols = len(input_vectors)+ 1\n",
    "    f, ax = plt.subplots(nrows=1, ncols = total_fig_cols, figsize=(total_fig_cols * 2,15))\n",
    "    for i in range(len(input_vectors)):\n",
    "        ax[i].plot(logs[input_vectors[i]], logs[DEPTH_col_name])\n",
    "    final_line = len(input_vectors)\n",
    "    im = ax[final_line].imshow(cluster, interpolation='none', aspect='auto',\n",
    "                               cmap=cmap_facies,vmin = 1,vmax = len(facies_colors))\n",
    "    \n",
    "    divider = make_axes_locatable(ax[final_line])\n",
    "    cax = divider.append_axes(\"right\", size=\"25%\", pad=0.05)\n",
    "    # cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "    cbar = plt.colorbar(im, cax=cax)\n",
    "    # cbar.set_label((17*' ').join(facies_labels))\n",
    "    cbar.set_label((4*' ').join(facies_labels))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-1):\n",
    "        ax[i].set_ylim(ztop,zbot)\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=3)\n",
    "        ax[i].set_xlabel(input_vectors[i])\n",
    "        ax[i].set_xlim(logs[input_vectors[i]].min(),logs[input_vectors[i]].max())\n",
    "        \n",
    "    ax[final_line].set_xlabel('Facies')\n",
    "    \n",
    "    for i in range(len(ax)-1):\n",
    "        ax[i].set_yticklabels([]);\n",
    "        \n",
    "    ax[final_line].set_yticklabels([])\n",
    "    ax[final_line].set_xticklabels([])\n",
    "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "make_facies_log_plot(\n",
    "    training_data,\n",
    "    facies_colors)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# make_facies_log_plot(\n",
    "#     training_data[training_data['Well Name'] == '58'],\n",
    "#     facies_colors)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Analysis Data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#count the number of unique entries for each facies, sort them by\n",
    "#facies number (instead of by number of entries)\n",
    "facies_counts = training_data['Facies'].value_counts().sort_index()\n",
    "#use facies labels to index each count\n",
    "facies_counts.index = facies_labels\n",
    "\n",
    "facies_counts.plot(kind='bar',color=facies_colors, \n",
    "                   title='Distribution of Training Data by Facies')\n",
    "facies_counts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "facies_color_map,list(reversed(facies_labels))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#save plot display settings to change back to when done plotting with seaborn\n",
    "# inline_rc = dict(mpl.rcParams)\n",
    "\n",
    "import seaborn as sns\n",
    "# sns.set()\n",
    "# sns.pairplot(training_data.drop(['Well Name','DEPTH','CAL','SP'],axis=1),hue='Facies', palette=facies_color_map, hue_order=list(reversed(facies_labels)))\n",
    "\n",
    "# #switch back to default matplotlib plot style\n",
    "# mpl.rcParams.update(inline_rc)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "correct_facies_labels = training_data['Facies'].values\n",
    "\n",
    "# feature_vectors = training_data.drop(['Formation', 'Well Name', 'DEPTH','Facies','FaciesLabels'], axis=1)\n",
    "feature_vectors = training_data[input_vectors]\n",
    "feature_vectors.describe()\n",
    "\n",
    "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "scaled_features = scaler.transform(feature_vectors)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "feature_vectors"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 归一化\n",
    "# X_train, X_test, y_train, y_test = train_test_split(\n",
    "#         scaled_features, correct_facies_labels, test_size=0.2, random_state=42)\n",
    "\n",
    "# 不归一化\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        feature_vectors, correct_facies_labels, test_size=0.2, random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y_train"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Training the Decision Tree classifier"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort='deprecated', ccp_alpha=0.0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn import tree as tree\n",
    "# based_method = 'gini' | 'entropy'\n",
    "based_method = 'entropy'\n",
    "tree_depth = 10\n",
    "class_weight = {0:0.22, 1:0.01,2:0.02,3:0.03,4:0.30,5:0.38,6:0.04}\n",
    "# clf = tree.DecisionTreeClassifier(criterion = based_method,max_depth = tree_depth)\n",
    "clf = tree.DecisionTreeClassifier(criterion = based_method,max_depth = tree_depth)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "training_img_file_saving_path = 'model_training_images/'\n",
    "child_dir_name =\"decision_tree\" + \"_\"  + \"training/\"\n",
    "model_training_img_file_saving_path = os.path.join(training_img_file_saving_path,child_dir_name)\n",
    "model_training_img_name =   \"decision_tree\"  + \"_\" + based_method + \"_tree_depth_\"+ str(tree_depth)\n",
    "    \n",
    "if not os.path.exists(model_training_img_file_saving_path):\n",
    "    os.makedirs(model_training_img_file_saving_path)\n",
    "        \n",
    "model_testing_image_name =  \"decision_tree\" + \"_\" + based_method + \"_tree_depth_\" + str(tree_depth)\n",
    "child_dir_name =  \"decision_tree\" + \"_\"  + \"testing/\"\n",
    "testing_img_file_saving_path = 'model_testing_images/'\n",
    "model_testing_img_file_saving_path = os.path.join(testing_img_file_saving_path,child_dir_name)\n",
    "if not os.path.exists(model_testing_img_file_saving_path):\n",
    "    os.makedirs(model_testing_img_file_saving_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "model_save_path = os.path.join(\"model/\", 'facies_decision-tree'  + \"_train/\")\n",
    "#model_save_path = os.path.join(\"model/\", 'facies_' + model_type.lower() + \"_train/\",child_dir_name)\n",
    "if os.path.exists(model_save_path):\n",
    "    model_path = model_save_path\n",
    "else:\n",
    "    os.makedirs(model_save_path)\n",
    "    model_path = model_save_path\n",
    "print(model_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "model_name = 'facies_tree_' + based_method \n",
    "model_file = model_path + model_name + '.pkl'\n",
    "print(\"model_name:\",model_name)\n",
    "print(\"model_file:\",model_file)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "clf.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plot Decision Tree"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "out_dot_file = model_path + model_name"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "dot_data = tree.export_graphviz(clf, out_file = out_dot_file + '.dot',feature_names=feature_vectors.columns,class_names=facies_labels,\n",
    "                               filled=True, rounded=True,special_characters=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "with open(out_dot_file + '.dot',encoding='utf-8') as fj:\n",
    "    source = fj.read()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "graph = graphviz.Source(source, format=\"png\") "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "graph"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "graph.encoding = 'utf-8'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "graph.render(out_dot_file)\n",
    "# out_dot_file"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Save Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# from sklearn.externals import joblib\n",
    "import joblib\n",
    "\n",
    "joblib.dump(clf, model_file)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Valid Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "predicted_labels = clf.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "clf.score(X_test,predicted_labels)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from classification_utilities import display_cm, display_adj_cm\n",
    "\n",
    "conf = confusion_matrix(y_test, predicted_labels)\n",
    "display_cm(conf, facies_labels, \n",
    "           display_metrics=True, hide_zeros=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(conf, xticklabels=facies_labels, yticklabels=facies_labels, annot=True, fmt=\"d\", annot_kws={\"size\": 20});\n",
    "plt.title(\"Confusion matrix\", fontsize=20)\n",
    "plt.ylabel('True label', fontsize=20)\n",
    "plt.xlabel('Predicted label', fontsize=20)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def accuracy(conf):\n",
    "    total_correct = 0.\n",
    "    nb_classes = conf.shape[0]\n",
    "    for i in np.arange(0,nb_classes):\n",
    "        total_correct += conf[i][i]\n",
    "    acc = total_correct/sum(sum(conf))\n",
    "    return acc"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
    "\n",
    "def accuracy_adjacent(conf, adjacent_facies):\n",
    "    nb_classes = conf.shape[0]\n",
    "    total_correct = 0.\n",
    "    for i in np.arange(0,nb_classes):\n",
    "        total_correct += conf[i][i]\n",
    "        for j in adjacent_facies[i]:\n",
    "            total_correct += conf[i][j]\n",
    "    return total_correct / sum(sum(conf))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "adjacent_facies = None\n",
    "if data_model == 1:\n",
    "    # adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
    "    adjacent_facies = [[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]]\n",
    "if data_model == 2:\n",
    "    # adjacent_facies = np.array([[0],[1], [0,1], [1,2,3], [4], [5], [5,6]])\n",
    "    adjacent_facies = [[0],[1], [0,1], [1,2,3], [4], [5], [5,6]]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print('Facies classification accuracy = %f' % accuracy(conf))\n",
    "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf, adjacent_facies))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def per_class_accuracy(conf):\n",
    "    per_class_correct = 0.\n",
    "    nb_classes = conf.shape[0]\n",
    "    per_acc = []\n",
    "    for i in np.arange(0,nb_classes):\n",
    "        acc = 0\n",
    "        per_class_correct = conf[i][i]\n",
    "        per_class_sum = 0\n",
    "        per_class_sum = per_class_sum + sum(conf[i])\n",
    "        print(per_class_sum)\n",
    "        acc = per_class_correct/per_class_sum\n",
    "#         acc = per_class_correct/sum(sum(conf))\n",
    "        per_acc.append(acc)\n",
    "    return per_acc"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "per_class_accuracy_list = per_class_accuracy(conf)\n",
    "for i in range(len(per_class_accuracy_list)):\n",
    "    print(i,' class facies classification accuracy = %f' % per_class_accuracy_list[i])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculate ROC"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "see: https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html?highlight=decision_function  \n",
    "if hasattr(clf, \"decision_function\"):  \n",
    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])  \n",
    "        else:  \n",
    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y_score = clf.predict_proba(X_test) \n",
    "y_score"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "y = tf.keras.utils.to_categorical(y_test,num_classes=len(facies_labels))\n",
    "y"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "#decision_function对于SVC返回每个样本距离决策边界的距离\n",
    "#decision_function对于LR返回预测样本的置信度分数——该样本与超平面的有符号距离\n",
    "\n",
    "n_classes = len(facies_labels)\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y[:, i], y_score[:, i])   \n",
    "    #y_test样例真实标签，y_score学习器预测的样例的概率 \n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])   \n",
    "    #计算ROC曲线下方的面积，fpr假正例率数组(横坐标)，tpr真正例率数组(纵坐标） "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y.ravel(), y_score.ravel())   #ravel函数将矩阵展开成向量\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y.ravel(),y.ravel().shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))   #np.concatenate将“特征维度相同数组”纵向拼接\n",
    "\n",
    "    # Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)   #np.zeros_like创建一个和参数all_fpr数组维度相同的全0数组\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])   \n",
    "        #interp一维线性插值，fpr和tpr是插值结点横纵坐标，all_fpr是已知中间节点横坐标(得到插值曲线后，求其纵坐标)\n",
    "    #https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html#numpy.interp\n",
    "\n",
    "    # Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize']=(8,6)\n",
    "plt.figure()\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc[\"micro\"]),\n",
    "             color='deeppink', linestyle=':')\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"], label='macro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc[\"macro\"]),\n",
    "             color='navy', linestyle=':')\n",
    "\n",
    "    # colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])   #python3里的无穷循环器\n",
    "# colors = facies_colors\n",
    "for i, color_use in zip(range(n_classes), facies_colors):\n",
    "    plt.plot(fpr[i], tpr[i], color = color_use, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(model_training_img_file_saving_path + model_training_img_name  + '_ROC.png', dpi=330,  bbox_inches='tight')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print('Facies classification accuracy = %f' % accuracy(conf))\n",
    "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf, adjacent_facies))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "display_cm(conf, facies_labels, \n",
    "           display_metrics=True, hide_zeros=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Applying the classification model to the blind data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "blind"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "clf = joblib.load(model_file)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y_blind = blind['Facies'].values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "well_features = blind.drop(['Well Name','Facies','DEPTH','CAL','SP'], axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# X_blind = scaler.transform(well_features)\n",
    "\n",
    "\n",
    "X_blind = well_features"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_blind)\n",
    "blind['Prediction'] = y_pred"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cv_conf = confusion_matrix(y_blind, y_pred)\n",
    "\n",
    "print('Optimized facies classification accuracy = %.2f' % accuracy(cv_conf))\n",
    "print('Optimized adjacent facies classification accuracy = %.2f' % accuracy_adjacent(cv_conf, adjacent_facies))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "per_class_accuracy_list_2 = per_class_accuracy(cv_conf)\n",
    "for i in range(len(per_class_accuracy_list_2)):\n",
    "    print(i,' class facies classification accuracy = %f' % per_class_accuracy_list_2[i])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "display_cm(cv_conf, facies_labels,\n",
    "           display_metrics=True, hide_zeros=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 考虑合并岩相\n",
    "display_adj_cm(cv_conf, facies_labels, adjacent_facies,\n",
    "               display_metrics=True, hide_zeros=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(conf, xticklabels=facies_labels, yticklabels=facies_labels, annot=True, fmt=\"d\", annot_kws={\"size\": 12});\n",
    "plt.title(\"Confusion matrix\", fontsize=12)\n",
    "plt.ylabel('True label', fontsize=12)\n",
    "plt.xlabel('Predicted label', fontsize=12)\n",
    "plt.savefig(model_testing_img_file_saving_path + model_testing_image_name + '_val_confusion_matrix.png', dpi=330,  bbox_inches='tight')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y_score = clf.predict_proba(X_blind) \n",
    "y_score"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "y = tf.keras.utils.to_categorical(y_blind-1,num_classes=len(facies_labels))\n",
    "y"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "#decision_function对于SVC返回每个样本距离决策边界的距离\n",
    "#decision_function对于LR返回预测样本的置信度分数——该样本与超平面的有符号距离\n",
    "\n",
    "n_classes = len(facies_labels)\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y[:, i], y_score[:, i])   \n",
    "    #y_test样例真实标签，y_score学习器预测的样例的概率 \n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])   \n",
    "    #计算ROC曲线下方的面积，fpr假正例率数组(横坐标)，tpr真正例率数组(纵坐标） "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y.ravel(), y_score.ravel())   #ravel函数将矩阵展开成向量\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "y.ravel(),y.ravel().shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))   #np.concatenate将“特征维度相同数组”纵向拼接\n",
    "\n",
    "    # Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)   #np.zeros_like创建一个和参数all_fpr数组维度相同的全0数组\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])   \n",
    "        #interp一维线性插值，fpr和tpr是插值结点横纵坐标，all_fpr是已知中间节点横坐标(得到插值曲线后，求其纵坐标)\n",
    "    #https://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html#numpy.interp\n",
    "\n",
    "    # Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=(8,6)\n",
    "plt.figure()\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc[\"micro\"]),\n",
    "             color='deeppink', linestyle=':')\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"], label='macro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc[\"macro\"]),\n",
    "             color='navy', linestyle=':')\n",
    "\n",
    "    # colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])   #python3里的无穷循环器\n",
    "# colors = facies_colors\n",
    "for i, color_use in zip(range(n_classes), facies_colors):\n",
    "    plt.plot(fpr[i], tpr[i], color = color_use, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(model_training_img_file_saving_path + model_training_img_name  + '_ROC.png', dpi=220,  bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def compare_facies_plot(logs, compadre, facies_colors):\n",
    "     #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by = DEPTH_col_name)\n",
    "    cmap_facies = colors.ListedColormap(\n",
    "            facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    \n",
    "    ztop=logs[DEPTH_col_name].min(); zbot=logs[DEPTH_col_name].max()\n",
    "    DEPTH_col = logs[DEPTH_col_name]\n",
    "    \n",
    "    cluster1 = np.repeat(np.expand_dims(logs[\"Prediction\"].values,1), 100, 1)\n",
    "    cluster2 = np.repeat(np.expand_dims(logs[facies_labels_col].values,1), 100, 1)\n",
    "    total_fig_cols = len(input_vectors)+ 2\n",
    "    f, ax = plt.subplots(nrows=1, ncols=len(input_vectors) + 2, figsize=(total_fig_cols*2,8))\n",
    "    for i in range(len(input_vectors)):\n",
    "        ax[i].plot(logs[input_vectors[i]], DEPTH_col)\n",
    "    final_line = len(input_vectors)\n",
    "\n",
    "    im1 = ax[final_line].imshow(cluster1, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin = 0,vmax = len(facies_colors))\n",
    "    im2 = ax[final_line+1].imshow(cluster2, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin = 0,vmax = len(facies_colors))\n",
    "    \n",
    "    divider = make_axes_locatable(ax[final_line+1])\n",
    "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "    cbar=plt.colorbar(im2, cax=cax)\n",
    "    cbar.set_label((25*' ').join(facies_labels))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-2):\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=3)\n",
    "        ax[i].set_xlabel(input_vectors[i])\n",
    "        ax[i].set_xlim(logs[input_vectors[i]].min()*0.98,logs[input_vectors[i]].max() * 1.02)\n",
    "        ax[i].set_ylim(zbot, ztop)\n",
    "        \n",
    "    # for i in range(len(ax)-2):\n",
    "    #     ax[i].invert_yaxis()\n",
    "    #     # ax[i].set_ylim(ztop,zbot)\n",
    "    #     ax[i].set_yticklabels([])\n",
    "    #     ax[i].set_ylim(zbot, ztop)\n",
    "    # ax[0].set_yticks([])\n",
    "    \n",
    "        \n",
    "  \n",
    "    ax[final_line].set_xlabel(compadre)\n",
    "    ax[final_line + 1].set_xlabel('Real Facies')\n",
    "    ax[final_line].set_yticklabels([])\n",
    "    ax[final_line].set_xticklabels([])\n",
    "    ax[final_line + 1].set_yticklabels([])\n",
    "    ax[final_line + 1].set_xticklabels([])\n",
    "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_facies_plot(blind, 'Prediction', facies_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Applying the classification model to new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>CAL</th>\n",
       "      <th>SP</th>\n",
       "      <th>GR</th>\n",
       "      <th>CNL</th>\n",
       "      <th>DT</th>\n",
       "      <th>DEN</th>\n",
       "      <th>MSFL</th>\n",
       "      <th>RS</th>\n",
       "      <th>RD</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012.3</td>\n",
       "      <td>9.3512</td>\n",
       "      <td>162.5924</td>\n",
       "      <td>133.0452</td>\n",
       "      <td>22.5484</td>\n",
       "      <td>98.0852</td>\n",
       "      <td>2.3972</td>\n",
       "      <td>4.5072</td>\n",
       "      <td>5.2958</td>\n",
       "      <td>4.8964</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.4</td>\n",
       "      <td>9.3496</td>\n",
       "      <td>162.6872</td>\n",
       "      <td>134.7578</td>\n",
       "      <td>22.5156</td>\n",
       "      <td>98.1316</td>\n",
       "      <td>2.3984</td>\n",
       "      <td>4.6278</td>\n",
       "      <td>5.1578</td>\n",
       "      <td>4.7058</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012.5</td>\n",
       "      <td>9.3360</td>\n",
       "      <td>162.8000</td>\n",
       "      <td>135.6130</td>\n",
       "      <td>22.4660</td>\n",
       "      <td>98.1660</td>\n",
       "      <td>2.4080</td>\n",
       "      <td>4.6110</td>\n",
       "      <td>5.1530</td>\n",
       "      <td>4.6730</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012.6</td>\n",
       "      <td>9.3232</td>\n",
       "      <td>162.8720</td>\n",
       "      <td>135.7674</td>\n",
       "      <td>22.5372</td>\n",
       "      <td>98.6876</td>\n",
       "      <td>2.4192</td>\n",
       "      <td>4.6086</td>\n",
       "      <td>5.2818</td>\n",
       "      <td>4.7770</td>\n",
       "      <td>2</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012.7</td>\n",
       "      <td>9.3194</td>\n",
       "      <td>162.9254</td>\n",
       "      <td>135.9260</td>\n",
       "      <td>22.5154</td>\n",
       "      <td>99.3286</td>\n",
       "      <td>2.4298</td>\n",
       "      <td>4.5486</td>\n",
       "      <td>5.4064</td>\n",
       "      <td>4.8762</td>\n",
       "      <td>2</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>2121.0</td>\n",
       "      <td>8.6860</td>\n",
       "      <td>167.7170</td>\n",
       "      <td>138.9920</td>\n",
       "      <td>19.5630</td>\n",
       "      <td>91.9960</td>\n",
       "      <td>2.5790</td>\n",
       "      <td>6.6650</td>\n",
       "      <td>5.4110</td>\n",
       "      <td>4.8130</td>\n",
       "      <td>3</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>2121.1</td>\n",
       "      <td>8.7100</td>\n",
       "      <td>167.8194</td>\n",
       "      <td>133.5352</td>\n",
       "      <td>19.7814</td>\n",
       "      <td>89.8688</td>\n",
       "      <td>2.5622</td>\n",
       "      <td>6.2690</td>\n",
       "      <td>5.7942</td>\n",
       "      <td>5.1658</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>2121.2</td>\n",
       "      <td>8.7220</td>\n",
       "      <td>167.7712</td>\n",
       "      <td>126.5028</td>\n",
       "      <td>19.7904</td>\n",
       "      <td>87.2976</td>\n",
       "      <td>2.5448</td>\n",
       "      <td>7.2152</td>\n",
       "      <td>6.4504</td>\n",
       "      <td>5.7964</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>2121.3</td>\n",
       "      <td>8.7248</td>\n",
       "      <td>167.6516</td>\n",
       "      <td>120.2804</td>\n",
       "      <td>19.4012</td>\n",
       "      <td>84.6108</td>\n",
       "      <td>2.5292</td>\n",
       "      <td>10.1568</td>\n",
       "      <td>7.3772</td>\n",
       "      <td>6.7212</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>2121.4</td>\n",
       "      <td>8.7214</td>\n",
       "      <td>167.5582</td>\n",
       "      <td>116.5122</td>\n",
       "      <td>18.5996</td>\n",
       "      <td>82.2284</td>\n",
       "      <td>2.5176</td>\n",
       "      <td>14.1060</td>\n",
       "      <td>8.4930</td>\n",
       "      <td>7.8698</td>\n",
       "      <td>6</td>\n",
       "      <td>YX-58</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1092 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DEPTH     CAL        SP        GR      CNL       DT     DEN     MSFL      RS      RD  Facies Well Name  Prediction\n",
       "0     2012.3  9.3512  162.5924  133.0452  22.5484  98.0852  2.3972   4.5072  5.2958  4.8964       6     YX-58           2\n",
       "1     2012.4  9.3496  162.6872  134.7578  22.5156  98.1316  2.3984   4.6278  5.1578  4.7058       6     YX-58           2\n",
       "2     2012.5  9.3360  162.8000  135.6130  22.4660  98.1660  2.4080   4.6110  5.1530  4.6730       6     YX-58           2\n",
       "3     2012.6  9.3232  162.8720  135.7674  22.5372  98.6876  2.4192   4.6086  5.2818  4.7770       2     YX-58           2\n",
       "4     2012.7  9.3194  162.9254  135.9260  22.5154  99.3286  2.4298   4.5486  5.4064  4.8762       2     YX-58           2\n",
       "...      ...     ...       ...       ...      ...      ...     ...      ...     ...     ...     ...       ...         ...\n",
       "1087  2121.0  8.6860  167.7170  138.9920  19.5630  91.9960  2.5790   6.6650  5.4110  4.8130       3     YX-58           3\n",
       "1088  2121.1  8.7100  167.8194  133.5352  19.7814  89.8688  2.5622   6.2690  5.7942  5.1658       6     YX-58           3\n",
       "1089  2121.2  8.7220  167.7712  126.5028  19.7904  87.2976  2.5448   7.2152  6.4504  5.7964       6     YX-58           2\n",
       "1090  2121.3  8.7248  167.6516  120.2804  19.4012  84.6108  2.5292  10.1568  7.3772  6.7212       6     YX-58           2\n",
       "1091  2121.4  8.7214  167.5582  116.5122  18.5996  82.2284  2.5176  14.1060  8.4930  7.8698       6     YX-58           3\n",
       "\n",
       "[1092 rows x 13 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ResultDataPath = \"facies_csv_results\"\n",
    "\n",
    "if os.path.exists(ResultDataPath) == False:\n",
    "    os.mkdir(ResultDataPath)\n",
    "\n",
    "ResultSaveDataPath = os.path.join(ResultDataPath, filename_A.split(\".csv\")[0] + \"DecisionTree.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "blind.to_csv(ResultSaveDataPath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Modified by Reference  \n",
    "Amato del Monte, A., 2015. Seismic Petrophysics: Part 1, The Leading Edge, 34 (4). doi:10.1190/tle34040440.1\n",
    "\n",
    "Bohling, G. C., and M. K. Dubois, 2003. An Integrated Application of Neural Network and Markov Chain Techniques to Prediction of Lithofacies from Well Logs, KGS Open-File Report 2003-50, 6 pp. pdf\n",
    "\n",
    "Dubois, M. K., G. C. Bohling, and S. Chakrabarti, 2007, Comparison of four approaches to a rock facies classification problem, Computers & Geosciences, 33 (5), 599-617 pp. doi:10.1016/j.cageo.2006.08.011\n",
    "https://github.com/seg/tutorials-2016/blob/master/1610_Facies_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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